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STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting
[article]
2019
arXiv
pre-print
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This paper proposes a novel Spatio-Temporal Graph Attention (STGRAT) that effectively captures the spatio-temporal dynamics in road networks. ...
Predicting the road traffic speed is a challenging task due to different types of roads, abrupt speed changes, and spatial dependencies between roads, which requires the modeling of dynamically changing ...
In this work, we propose a novel Spatio-Temporal Graph Attention Network (STGRAT) for predicting traffic speed, entirely based on a self-attention mechanism. ...
arXiv:1911.13181v1
fatcat:tbkzbzxsabci7ox3o62gqitwpa
STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel deep learning model for traffic forecasting, named Multi-Context Aware Spatio-Temporal Joint Linear Attention (STJLA), which applies linear attention to the spatio-temporal ...
Previous works combined graph convolution networks (GCNs) and self-attention mechanism with deep time series models (e.g. recurrent neural networks) to capture the spatio-temporal correlations separately ...
In this paper, we propose a novel Multi-Context Aware Spatio-Temporal Joint Linear Attention Network (STJLA) for traffic forecasting, which design a spatio-temporal joint mode that combines the sub-graphs ...
arXiv:2112.02262v1
fatcat:e2bagkvdpjdu7awhyoig2pbxeu
Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network
[article]
2022
arXiv
pre-print
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short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph ...
Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the ...
Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of IJCAI, 2018. ...
arXiv:2112.02740v2
fatcat:xbaudqqkbzhz5jjiva3gmkx33y
Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies
2021
IET Intelligent Transport Systems
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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Traffic prediction that aims to model the dynamic change of the traffic system is a well-studied spatial-temporal prediction problem, and multi-step traffic forecasting on road network is a crucial task ...
To better capture the complex spatial-temporal dependencies and forecast traffic conditions on road networks, a multi-step prediction model named Spatial-Temporal Attention Wavenet (STAWnet) is proposed ...
is an end-to-end solution for traffic forecasting that captures spatial, short-term, and long-term periodical dependencies. • ST-GRAT: Spatiao-Temporal GRaph ATtention [33] , which uses spatial attention ...
doi:10.1049/itr2.12044
fatcat:4fr5numrcjhulmtfa6pstwyxeu